Lorenzo Carlin

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This paper proposes a novel pixel-based system for the supervised classification of very high geometrical (spatial) resolution images. This system is aimed at obtaining accurate and reliable maps both by preserving the geometrical details in the images and by properly considering the spatialcontext information. It is made up of two main blocks: 1) a novel(More)
This paper presents a novel support vector machine classifier designed for subpixel image classification (pixel/spectral unmixing). The proposed classifier generalizes the properties of SVMs to the identification and modeling of the abundances of classes in mixed pixels by using fuzzy logic. This results in the definition of a fuzzy-input fuzzy-output(More)
In this paper we present a novel fuzzy input-fuzzy output support vector machine (F<sup>2</sup>-SVM) classifier, which is able to process fuzzy information given as input to the classification algorithm and to produce fuzzy classification outputs. The main novelties of the proposed F<sup>2</sup>-SVM consist of: i) simultaneous and proper management of both(More)
Although underestimated in practice, the small/unrepresentative sample problem is likely to affect a large segment of real-world remotely sensed (RS) image mapping applications where ground truth knowledge is typically expensive, tedious, or difficult to gather. Starting from this realistic assumption, subjective (weak) but ample evidence of the relative(More)
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